Smartphone and Tablet Applications for Crime Scene Investigation: State of the Art, Typology, and Assessment Criteria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of applications on mobile devices is gradually becoming a new norm in everyday life, and crime scene investigation is unlikely to escape this reality. The article assesses the current state of research and practices by means of literature reviews, semistructured interviews, and a survey conducted among crime scene investigators from Canada and Switzerland. Attempts at finding a particular strategy to guide the development, usage, and evaluation of applications that can assist crime scene investigation prove to be rather challenging. Therefore, the article proposes a typology for these applications, as well as criteria for evaluating their relevance, reliability, and answer to operational requirements. The study of five applications illustrates the evaluation process. Far away from the revolution announced by some stakeholders, it is required to pursue scientific and pragmatic research to set the theoretical foundations that will allow a significant contribution of applications to crime scene investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it